Recommender systems (RSs) are powerful automatic tools that help users find interesting multimedia content (e.g., movie, music, games, . . . ) from wide storage directories and online media services.
RSs are characterized by the capability of filtering large information spaces and selecting the items that are likely to be more interesting and attractive to a specific user. Particularly, they play a significant role in on-demand TV channels, IP Television and video-on-demand web applications (e.g. YouTube and Netflix) where very large catalogues of movies are available to the users: the main goal of RSs is to find and recommend to users the movies that are likely to be interesting to them. In other words, the RSs shall match the features of a number of video items with a profile of the intended users, so as to rank the items in their order of preference to the user.
Recommendation systems can rely substantially on two different approaches, either collaborative systems—requiring interaction with and between the users—or content-based systems—where suggestions are given based on a number of known features of the items. In the present application, only this second approach is considered.
A prerequisite for content-based RSs is the availability of information about “explicit” content features of the items. In movie items, such features are associated to the items as structured meta-information (e.g., movie genre, director, cast and so on) or unstructured meta-information (e.g., plot, tags and textual reviews).
As it is apparent, this requirement represents one of the main concerns of these systems, since collecting appropriate information about features is a time and resource consuming task which, in the best conditions, at least results in a time delay between the availability of the item and when it is properly tagged to be used in a RS. This is often called also “cold start” problem, which result in poor recommendations when many items, without proper meta-tags, are entered in the catalogue in a short time delay.
Accordingly, there is a demand for a recommendation system which is able to automatically detect a number of relevant features from the item to be suggested.
The prior art already provides a number of systems aimed to obtain an automatic recommendation using a content-based RSs.
For example, KR20080107143 discloses a method for recommending music and a moving picture based on an audio fingerprint technology using signal processing.
US20120102033 discloses a learning system using loosely annotated multimedia data on the web, analyses it in various signal domains and builds an association graph which basically comprises visual signals, audio signals, text phrases and the like that capture a multitude of objects, experiences and their attributes and the links among them.
WO 2014054025 discloses a method for recommending multimedia contents through a multimedia platform comprising a plurality of multimedia contents observable through at least one user interface.
US 20090006368 is describing a method wherein the source videos are directly compared to a user selected video to determine relevance, which is then used as a basis for video recommendation.
All these systems are still not satisfactory since they take into consideration too many aspects of an item, resulting in a rather complex dataset which it is then difficult to handle and gives non reliable results. Some of them require still a certain manual intervention.
Moreover, U.S. Pat. No. 6,741,655 discloses a recognition system for permitting a user to locate one or more video objects from one or more video clips over an interactive network.